Rapid Profiling of the Plasma Proteome and Machine Learning Analytics for Non-Invasive Diagnosis of Alzheimer's Disease

血浆蛋白质组的快速分析和机器学习分析用于阿尔茨海默病的无创诊断

• 批准号: 10002170
• 负责人: Asim Siddiqui
• 金额: $ 91.03万
• 依托单位: SEER, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10002170
• 关键词: Address; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease patient; American; Amyloid beta-42; Amyloid beta-Protein; Binding; Biological; Biological Markers; Blood; Brain; Caring; Cause of Death; Cerebrospinal Fluid; Charge; Classification; Clinical; Clinical Research; Clinical Trials; Cluster Analysis; Cognitive; Communication; Complex; Consumption; Data; Data Science; Detection; Development; Diagnostic; Diagnostic tests; Disease; Early Diagnosis; Early identification; Early treatment; Elderly; Enzyme-Linked Immunosorbent Assay; Epitopes; Family member; Fingerprint; Fractionation; Gender; Health Care Costs; Health Status; Hydrophobicity; Individual; Industry; Investments; Laboratories; Lead; Legal patent; Libraries; Liquid substance; Literature; Machine Learning; Malignant Neoplasms; Manuscripts; Mass Spectrum Analysis; Medical; Methods; Monitor; Nanotechnology; Nature; Neuropsychological Tests; Patients; Pattern; Phase; Plasma; Plasma Proteins; Population; Privatization; Procedures; Process; Property; Protein Analysis; Proteins; Proteome; Proteomics; Publishing; Puncture procedure; Quality of life; Reproducibility; Sampling; Scientist; Sensitivity and Specificity; Small Business Innovation Research Grant; Spinal Puncture; Surface; Techniques; Technology; Testing; Thinness; Time; Translating; United States; Validation; Work; base; blind; blood-based biomarker; clinical practice; cohort; comorbidity; cost; cost effective; cost efficient; disorder control; economic impact; high throughput analysis; innovation; liquid chromatography mass spectrometry; mild cognitive impairment; nanoparticle; neuroimaging; noninvasive diagnosis; novel; novel therapeutics; programs; response; screening; social; tau Proteins; therapy outcome

ABSTRACT The main objective of this project is to develop an innovative blood-based test for highly sensitive and specific, non-invasive and cost-efficient diagnosis of Alzheimer's disease (AD), which would leverage Seer's proprietary Proteograph platform enabled by the convergence of nanotechnology, protein corona, proteomics, and data science. Beyond neuropsychological testing, two approaches have thus far been clinically validated for AD detection, including neuroimaging and analysis of cerebrospinal fluid (CSF)-based biomarkers (e.g., amyloid-β or Aβ). In contrast to the neuroimaging (which is expensive and time-consuming) and CSF analysis (which is less expensive, but involves an invasive lumbar puncture procedure), a blood-based test for AD diagnosis has the potential to be dramatically less costly and easier to implement. Nevertheless, the search for reliable blood- based biomarkers has been challenging and the blood-based detection using ELISA or other epitope-based methods that go after a few biomarkers (e.g., Aβ42 or Tau) have not been successful, presumably owing to the vast dynamic range and high complexity of the plasma components. We have recently demonstrated that our multi-nanoparticle (NP) protein corona technology can facilitate broad and deep profiling of plasma proteome, and by combining with machine learning approaches, could lead to the development of Proteograph classifiers for highly accurate detection of different diseases including AD. As compared to current mass spectrometry- based proteomic techniques that require complex and time-consuming depletion or fractionation workflows for detection of low abundance/rare proteins, our multi-NP protein corona strategy is fast and high-throughput for analysis of the vast body of information in the proteome. In this Direct Phase II project, we will build upon the proof-of-concept studies to further test how Seer's Proteograph platform can be applied to develop a robust blood-based test to detect AD. Specifically, we will identify a panel (~6-10) of NPs from Seer's NP library for broad and deep coverage of the plasma proteome of AD patients (Aim 1); develop Proteograph classifiers and identify the proteins critical for classification through machine learning of the proteomic data generated from the panel of NPs with a cohort of 150 plasma samples of AD and healthy controls (Aim 2); and validate the accuracy of the detection test (based on the important proteins identified in Aim 2) in a separate blind cohort of 450 Aβ-positive AD patients and healthy controls (Aim 3). We expect that the successful completion of this SBIR project will lead to the clinical use of a blood-based AD test, which could further benefit earlier treatment, therapeutic outcomes, and health costs and quality of life for the elderly.

摘要 该项目的主要目标是开发一种创新的基于血液的检测方法，用于高度敏感和特异的检测， 对阿尔茨海默病(AD)的非侵入性和经济高效的诊断，这将利用Seer的专利 融合纳米技术、蛋白质日冕、蛋白质组学和数据的蛋白质图谱平台 科学。除了神经心理测试之外，到目前为止，有两种方法已经在临床上得到了AD的验证 检测，包括神经成像和脑脊液生物标记物(例如，淀粉样蛋白-β)的分析 或β)。与神经成像(昂贵且耗时)和脑脊液分析(这是 更便宜，但涉及侵入性腰椎穿刺术)，一种基于血液的AD诊断测试 有可能大大降低成本，更容易实施。然而，寻找可靠的血液- 基于生物标记物的检测一直具有挑战性，而基于血液的检测使用ELISA或其他基于表位的检测 寻找一些生物标记物(例如，β42或Tau)的方法并不成功，可能是因为 等离子体元件的动态范围大、复杂性高。我们最近展示了我们的 多纳米粒子(NP)蛋白质电晕技术可以促进血浆蛋白质组的广泛和深入的图谱， 并通过与机器学习方法相结合，可以导致蛋白质图谱分类器的发展 用于高精度检测包括阿尔茨海默病在内的各种疾病。与目前的质谱学相比- 基于蛋白质组学技术，需要复杂且耗时的耗尽或分级工作流 对于低丰度/稀有蛋白的检测，我们的多NP蛋白电晕策略是快速和高通量的 分析蛋白质组中的大量信息。在这个直接第二阶段的项目中，我们将在 概念验证研究，以进一步测试Seer的蛋白质图谱平台如何应用于开发健壮的 以血液为基础的检测AD。具体地说，我们将从Seer的NP库中确定一组(~6-10)NP，用于 广泛和深入地覆盖AD患者的血浆蛋白质组(目标1)；开发蛋白质谱分类器和 通过机器学习生成的蛋白质组数据来识别对分类至关重要的蛋白质 由150个AD和健康对照的血浆样本组成的NPs小组(目标2)；并验证 检测测试的准确性(基于目标2中确定的重要蛋白质)在单独的盲群队列中 450A-β阳性AD患者和健康对照组(目标3)。我们期待着这一项目的成功完成 SBIR项目将导致基于血液的AD测试的临床使用，这可能进一步有利于早期治疗， 治疗结果，以及老年人的健康成本和生活质量。